Prepare for a career in data analysis

data analysis It is a great and growing profession, and increasingly capable of impressive salaries as more and more companies realize the many ways in which data analysis can make a huge difference to their business operations and bottom line. But what should you do if you feel the call of data analysis and want to make it your career? What are the steps you need to take on your way to your chosen profession?

First – be sure. One of the main reasons data analytics has started to attract the big bucks is that it is by no means an easy journey. It involves intense study of complex subjects, and you’ll need a proficiency in maths and especially statistics, so if these things are turning you off, it’s wise – and kind to everyone – to think again and choose a different path.

If you are still convinced that this data analysis is the right path for you, then:

Master the basics.

Statistics will be your friends, companions, and way forward in data analysis. Learn to master them, question them, and get comfortable arguing between them – start with statistics in Excel, in BI, whatever you can think of, but get started, and get comfortable in the world of statistical analysis and manipulation.

This includes a range of disciplines, such as measuring variances, determining probability distributions, testing hypotheses and so on. It’s also a good idea to get at least a basic grounding in SQL, as you’ll likely spend a lot of time in your career querying databases, so the sooner you get that under your belt, the better – and very likely, the better. Your career progresses faster.

Choose a language.

While this may take some time, and a few samples from the array of languages ​​out there, it’s wise to find either one primary language, or a combination of languages ​​that you’re really comfortable with. This is because data analysis roles frequently demand specialization in one language – such as Python – or another. By narrowing your focus, you will stand a better chance of getting your foot on the career ladder in data analysis, and there should always be time as your career progresses to add other languages ​​as they either appeal to you, or prove essential in order to advance your career path as it develops.

education path.

As with most career paths—especially those that lead to higher salaries, rewarding, and more complex job opportunities—you often need to update your certifications to get in the door. This usually means getting at least a bachelor’s degree, and maybe even a master’s degree in data analysis in college.

Remember the part where we told you to make sure you wanted to go down that road? A bachelor’s degree usually takes two to three years of full-time work, a master’s degree at least another year on top, and these days you can explain paying at least five figures, possibly as many as six, for the training you get. It will prepare you for a life and career in data analysis.

This means that you need to think carefully about your finances before embarking on your data analysis degree.

You are can Do some training and certification online, which will take a fraction of the time and will cost you a lot less initial expense. But there are a few things to keep in mind if you decide to go this route. First, the degree to which employers will take your degree seriously will likely depend on the level and “weight” of your qualifications. Study data analysis at Harvard for three years, and the qualification you’ll leave with is likely to open some serious and interesting doors once you’re done. Get your qualifications on and you’ll likely spend more time on smaller, more run jobs, building your portfolio of project work to show up to bigger and better employers.

Like many modern disciplines, data analysis is part of the knowledge economy. The different roads you can take are equally valid, but they give you different journeys. Balancing getting your qualifications depends on matching what you can afford out of pocket, and the amount of time you are willing to spend putting in significant effort to build your post-qualification reputation.

Build your portfolio.

Wherever you get from, once you get your shiny new certification, technically you’re a data analyst, and you can call your own. But a lot of companies will also want to see your skills put into practice. This means building your portfolio. Work on some projects, either individually or in groups, that you think are good milestones to show how well you’ve exchanged data.

As a way to enhance your portfolio, there are freely available datasets that you can use to create your own projects in your spare time, so you have additional projects to show potential employers that highlight the creative ways you approach data, the elegance you develop in your argumentation and storytelling skills, and the skills comprehensive that you have at your fingertips in your chosen language or languages.

Be prepared for the long climb.

Like all jobs in the knowledge economy, you’ll likely start in entry-level data analysis roles, most likely on a team. Be realistic in your initial post-qualification job search, and focus on roles that make use of your skills and qualifications, and that spark or interest you.

Above all, be prepared to work your way through the ranks – whatever you think you have learned and achieved before you actually start. Action Your job is in data analysis, the likelihood is that it’s almost nothing compared to the work experience with the demands and deadlines of real-world data analysis work. Learn, keep practicing and expanding your skill set, and you should have a fun and profitable career in data analysis.

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